Journal
DISPLAYS
Volume 69, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.displa.2021.102058
Keywords
Stereoscopic image quality assessment; Quaternion wavelet transform; Feature extraction; Heterogeneous ensemble learning; Rotated feature space
Ask authors/readers for more resources
This paper proposes a no-reference stereoscopic image quality assessment model that extracts quality information from diverse image features using heterogeneous ensemble learning. It discovers various "quality-aware" features in the quaternion wavelet domain and predicts quality scores using a combination of support vector regression, extreme learning machine, and random forest models. Experimental results show the accuracy and robustness of this model on four public databases.
As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology. In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning 'quality-aware' features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of 'quality-aware' features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available